code: def dask_task_wrapper(
      func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *args
  ):
      """
      A Ray remote function acting as a Dask task wrapper. This function will
      repackage the given flat `args` into its original data structures using
      `repack`, execute any Dask subtasks within the repackaged arguments
      (inlined by Dask's optimization pass), and then pass the concrete task
      arguments to the provide Dask task function, `func`.
  
      Args:
          func: The Dask task function to execute.
          repack: A function that repackages the provided args into
              the original (possibly nested) Python objects.
          key: The Dask key for this task.
          ray_pretask_cbs: Pre-task execution callbacks.
          ray_posttask_cbs: Post-task execution callback.
          *args (ObjectRef): Ray object references representing the Dask task's
              arguments.
  
      Returns:
          The output of the Dask task. In the context of Ray, a
          dask_task_wrapper.remote() invocation will return a Ray object
          reference representing the Ray task's result.
      """
      if ray_pretask_cbs is not None:
          pre_states = [
              cb(key, args) if cb is not None else None
              for cb in ray_pretask_cbs
          ]
      repacked_args, repacked_deps = repack(args)
      # Recursively execute Dask-inlined tasks.
      actual_args = [
          _execute_task(a, repacked_deps) for a in repacked_args
      ]
      # Execute the actual underlying Dask task.
      result = func(*actual_args)
  
      if ray_posttask_cbs is not None:
          for cb, pre_state in zip(ray_posttask_cbs, pre_states):
              if cb is not None:
                  cb(key, result, pre_state)
  
      return result
  
imports: import atexit
  import threading
  from collections import defaultdict
  from collections import OrderedDict
  from dataclasses import dataclass
  from multiprocessing.pool import ThreadPool
  from typing import Optional
  import ray
  import dask
  from dask.core import istask, ishashable, _execute_task
  from dask.system import CPU_COUNT
  from dask.threaded import pack_exception, _thread_get_id
  from ray.util.dask.callbacks import local_ray_callbacks, unpack_ray_callbacks
  from ray.util.dask.common import unpack_object_refs
  from ray.util.dask.scheduler_utils import get_async, apply_sync
label_desc: Use image node_id 7 for calling a global function with example usage: dask_task_wrapper(func, repack, key, ray_pretask_cbs, ray_posttask_cbs) and returns: result